To satisfy the average user, a voice interface to a search engine must recognize spoken queries, and must return highly relevant search results. Several problems exist in designing satisfactory voice interfaces. Current speech recognition technology has high word error rates for large vocabulary sizes. There is very little repetition in queries, providing little information that could be used to guide the speech recognizer. In other speech recognition applications, the recognizer can use context, such as a dialogue history, to set up certain expectations and guide the recognition. Voice search queries lack such context. Voice queries can be very short (on the order of only a few words or single word), so there is very little information in the utterance itself upon which to make a voice recognition determination.
Current voice interfaces to search engines address the above problems by limiting the scope of the voice queries to a very narrow range. At every turn, the user is prompted to select from a small number of choices. For example, at the initial menu, the user might be able to choose from “news,” “stocks,” “weather,” or “sports.” After the user chooses one category, the system offers another small set of choices. By limiting the number of possible utterances at every turn, the difficulty of the speech recognition task is reduced to a level where high accuracy can be achieved. This approach results in an interactive voice system that has a number of severe deficiencies. It is slow to use, since the user must navigate through may levels of voice menus. If the user's information need does not match a predefined category, then it becomes very difficult or impossible to find the information desired. Moreover, it is often frustrating to use, since the user must adapt his/her interactions to the rigid, mechanical structure of the system.
Therefore, there exists a need for a voice interface that is effective for search engines.